• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.418-418
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• 2000

This paper proposes a path planning algorithm for mobile robot. To generate an optimal path and minimum time path for a mobile robot, we use the Genetic Algorithm(GA) and Visibility Graph. After finding a minimum-distance between start and goal point, the path is revised to find the minimum time path by path-smoothing algorithm. Simulation results show that the proposed algorithms are more effective.

• Journal of Korean Society of Transportation
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• v.17 no.2
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• pp.163-178
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• 1999

Conventional shortest path searching a1gorithms are based on the partial searching method such as Dijsktra, Moore etc. The a1gorithms are effective to find a shortest path in mini-modal condition of a network. On the other hand, in multi-modal case they do not find a shortest path or calculate a shortest cost without network expansion. To copy with the problem, called Searching Area Problem (SAP), a global searching method is developed in this paper with Genetic Algorithm. From the results of two examples, we found that the a1gorithm is useful to solving SAP without network expansion.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.133-141
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• 2001

Rapid development in computer and network technology these days has created in environment in which decisions for manufacturing companies can be made in a much broader perspective. Especially, better decisions on production and distribution planning(PDP) problems can be made laking advantage of real time information from all the parties concerned. However, since the PDP problem-a core part of the supply chain management- is known to be the so-called NP-hard problem, so heuristic methods are dominantly used to find out solutions in a reasonable time. As one of those heuristic techniques, many previous studios considered genetic a1gorithms. A standard genetic a1gorithm applies rules of reproduction, gene crossover, and mutation to the pseudo-organisms so the organisms can pass along beneficial and survival-enhancing trails to a new generation. When it comes to representing a chromosome on the problem, it is hard to guarantee an evolution of solutions through classic a1gorithm operations alone, for there exists a strong epitasis among genes. To resolve this problem, we propose a hybrid genetic a1gorithm based on Silver-Meal heuristic. Using IMS-TB(Intelligent Manufacturing System Test-bed) problem sets. the good performance of the proposed a1gorithm is demonstrated.